Each engagement is scoped to the specific engineering problem — from initial model
selection through production deployment, evaluation, and long-term operational reliability.
Services span the full spectrum of applied AI and machine learning systems.
01 — LLM Optimization
Large Language Model Optimization & Deployment
Deploying and optimizing modern open-source language models for practical applications.
Reducing infrastructure requirements while maintaining model capability through
quantization, adapter-based customization, and efficient inference pipeline design.
Model Size Reduction (Quantization)
Efficient Fine-Tuning (LoRA/QLoRA)
GPU-Accelerated Inference
Performance Benchmarking
02 — AI Infrastructure
Quantized AI Infrastructure
Building infrastructure that allows advanced models to run efficiently on available
hardware. GGUF conversion workflows, GPU-accelerated inference using llama.cpp,
VRAM profiling, and scalable distributed inference architectures.
GGUF Model Compression
Local Inference (llama.cpp)
GPU Memory Optimization
Multi-GPU Inference Scaling
03 — ML Pipelines
Machine Learning Pipeline Engineering
End-to-end machine learning systems from raw data to reliable predictive models.
Data preprocessing, feature engineering, hyperparameter optimization, probabilistic
calibration, and model interpretability — all designed for operational transparency.
Predictive Feature Design
Automated Model Tuning
Prediction Confidence Tuning
Model Explainability
04 — Evaluation
AI Evaluation & Model Reliability
Rigorous evaluation frameworks that measure model behavior under realistic conditions.
Deterministic evaluation harnesses, structured output validation, prompt robustness
testing, and golden-fixture systems for repeatable, trustworthy deployment.
Automated Evaluation Frameworks
Structured Output Validation
Prompt Stress Testing
Repeatable Benchmark Fixtures
05 — Synthetic Data
Synthetic Data & Privacy-Preserving AI
Systems that enable dataset generation while protecting sensitive information.
Structured placeholder token systems, privacy-preserving training workflows,
and closed-loop synthetic data generation and evaluation pipelines.
Synthetic Dataset Generation
Privacy-Preserving Workflows
Personal Data Masking Systems
Self-Validating Data Pipelines
06 — Agentic AI
Agentic AI Workflows & Evaluation
Design and implementation of autonomous multi-step AI pipelines with tool use,
memory, and decision chaining. Includes systematic benchmarking and evaluation
frameworks for agentic system performance, reliability, and safety boundaries.
Autonomous Multi-Step Pipelines
Dynamic Tool Integration
Agent Behavior Evaluation
Operational Safety Constraints
07 — Automation
AI Automation & Systems Integration
Python-based orchestration systems that integrate AI capabilities into operational
workflows. Automated ML pipelines, GPU resource management, AI-driven content
workflows, and end-to-end automation for AI and media infrastructure.
Python Workflow Orchestration
GPU Resource Management
End-to-End Workflow Automation
AI Media Infrastructure
08 — Scalable Media
Scalable Media Production & AI Distribution Infrastructure
Automated media production and distribution systems that combine AI, professional
media tools, and workflow automation into unified infrastructure. Integrating
Adobe Premiere Pro, After Effects, Audition, and AI-assisted narration with
Python-based automation services — moving organizations from isolated content
creation to repeatable, scalable publishing systems across YouTube, LinkedIn,
and other platforms.
Premiere Pro / After Effects
AI-Generated Narration (TTS)
Automated Watch-Folder Processing
Automated Metadata Pipelines
Multi-Platform Video Distribution
End-to-End Production Orchestration
